Project Summary/Abstract Cells perceive and respond to their environment by engaging receptors and transmitting intracellular messages via signal transduction cascades. This process is largely controlled by networks of proteins that bind, dissociate, and advance signal progression along biochemical pathways. Signalosomes can be part of this process, formed when proteins acting as network hubs orchestrate interactions with other protein nodes to control activation of various signaling pathways simultaneously. It is this modular, conditional interconnectivity between proteins and pathways that is largely responsible for providing the logic circuits required for signal transmission, synthesizing instructions for discrete cellular responses from multiple signaling inputs. But despite its high biological importance, the empirical assessment of signaling protein complexes at the network level is severely restricted by technological limitations, especially in the case of small clinical samples that provide low amounts of biomaterial for assessment. We propose to advance a new strategy, q-PiSCES, to allow molecular quantification of proteins that can be detected in signaling complexes from physiologic samples, such as those from human clinical patients or pre-clinical mouse models. Q-PiSCES will initially be developed for a collection of 10 protein targets with 55 unique pairwise associations in the T cell antigen receptor (TCR) signalosome that is known to exert strong control of immune responses (Specific Aim 1). Biostatistical analysis will feed into a Bioinformatics pipeline to focus on three specific parameters of protein complexes: protein abundance, clustering of identical proteins, and heterotypic protein co-associations (Specific Aim 2). We will field-test q-PiSCES by applying it to the analysis of human protein complexes associated with the autoimmune disease, Alopecia Areata (Specific Aim 3). Together, q-PiSCES stands to dramatically increase the ability to observe, measure, and study network patterns of physiologic protein complexes. We propose that the patient-derived q-PiSCES data will exemplify a new strategy for analyzing these complexes, and illustrate its general applicability to many fields of study and classes of disease.